Method for processing seismic data

ABSTRACT

The invention relates to a method for processing a seismic 2-D or 3-D measurement data set comprised of a multitude of seismic traces each comprising a series of data points provided with amplitude values. The inventive method is characterized by the following steps: Converting the measurement data set into a binary data set in which either the number “0” is assigned to each data point when an amplitude value is less than a predetermined threshold value, or else the number “1” is assigned to each data point; including a vicinity which is located around each binarized data point and which is defined by a predetermined cell size in a similarity analysis, hereby a value is assigned to each data point. The value reflects the degree of similarity of the binary data values in the respective allocated cell and, alternatively corresponds to the larger number of data points having the same binary value of the cell, to the sum of the respective larger number of data points having the same binary value in the horizontal planes containing the many data points, or corresponds to the sum of the respective larger number of data points having the same binary value in each trace of the cell.

CROSS REFERENCE TO RELATED APPLICATIONS

Applicants claim priority under 35 U.S.C. §119 of GERMAN Application No.199 04 347.7, filed: Feb. 3, 1999. Applicants also claim priority under35 U.S.C. §120 of PCT/DE00/00139, filed: Jan. 12, 2000. Theinternational application under PCT article 21(2) was not published inEnglish.

BACKGROUND OF THE INVENTION

The invention relates to a method for processing a seismic 2-D or 3-Dmeasurement data set comprising a great number of seismic traces eachcomprising a series of data points occupied by amplitude values.

Methods for exploring seismic data are employed worldwide for thepurpose of obtaining additional knowledge about the spread ofsubterranean geological structures in addition to information gatheredfrom sunk drilling holes. Owing to the information obtained from seismicdata it is often possible to dispense with further cost-intensiveexploration drilling operations, or to restrict their number to aminimum.

Sensors (geophones/hydrophones) are employed in the seismic explorationof subterranean structures that are lined up one after the other(2-D-seismology), or which are receiving sound waves. Such waves areexcited by a seismic source, for example by an explosive charge,vibratory excitement or air guns, and are partly reflected back to thesurface by the beds of the earth. The waves are registered by thesensors on the surface and recorded in the form of time series. Such atime series represents the seismic energy received in the form ofamplitude variations. It is digitally stored and consists of uniformlyarranged data points (samples), which are characterized by the time andthe associated amplitude values. Such a time series is referred to alsoas a seismic trace. The measurement series migrates over the area to beexplored, so that a seismic 2-D profile is recorded with such anarrangement.

The goal of the subsequent processing operation is to suppress thenoise, for example by batch processing, or with the help of filtersemployed in a targeted manner. The results so obtained are verticalprofiles in which amplitudes and propagation times as well as attributesderived from amplitudes are represented that serve as the basis forfurther geological evaluation. The geological strata can be observed ona profile by lining up the amplitudes laterally.

If the data are recorded not only along a line but in a flat matrix, athree-dimensional data volume is obtained. In the case of the 3-Dvolume, an amplitude value is assigned to any desired point in theunderground structure that is described, for example by Cartesiancoordinates. The vertical direction is measured in time (soundpropagation time).

Large amounts of data (several gigabytes) are collected in such aprocess, which are stored and subjected to processing before the actualinterpretation is possible with respect to, for example furtherexploration of the subterranean structures. Such processes requirecomprehensive computer resources and software for processing andcorrecting the received signals. The result is a seismic volume in theform of a 3-D data set that represents physical properties of theexplored subterranean structure in a seismic reproduction.

Any desired sections such as, for example vertical profiles andhorizontal slices relating to different depths of exploratory drillingcan be extracted from said data set, which are then interpreted bygeophysicists and geologists in the further course of the explorationoperation. As such interpretation of the seismic reproductions soobtained substantially comprises an optical correlation, attempts havebeen made to automate such reproductions through subjectiveinterpretation depending on one or a number of interpreters.

A method for seismic data processing is known from WO 96/18915, by whicha seismic 3-D volume is divided in a great number of horizontal slicesthat are vertically standing one on top of the other and spaced fromeach other, whereby at least one slice is divided in a multitude ofcells. In this process, each cell has at least 3 trace sections, wherebythe first and the second trace sections are arranged in a vertical planein the direction of the profile (inline), and the third trace sectionwith the first trace section is arranged in a vertical planesubstantially perpendicular to the direction of the profile (crossline).A cross correlation is subsequently carried out between each two tracesections in the two vertical planes, which results in inline- andcrossline-values that are dependent upon the inclination of the beds ofthe earth. Combining such values in one cell results in a coherencevalue for the cell that is assigned to a data point of the cell. The endresult in turn is a 3-D data volume from which any desired sections canbe extracted and represented.

A method and a device for seismic data processing by means of coherencycharacteristics is known from EP 0 832 442 A1. In said process, aseismic volume is divided in horizontal slices in a manner similar tothe method employed in the aforementioned published document, and saidslices are in turn divided in cells. Said cells have the shape of cubesin the simplest case. Based on the at least two trace sections presentin a cell, a correlation matrix is formed representing in each case thesum of the differences between the inner and the outer products of theset of values based on the trace sections. The quotient based on thehighest inherent value of the matrix and the sum of all inherent valuesis then computed as the measure for the coherence. A 3-D volumeconsisting of coherence values is subsequently obtained in turn as theresult.

Furthermore, EP 0 796 442 A1 relates to a method and a device forseismic data processing, by which a coherence method is carried out thatis based on a semblance analysis. In a manner that is similar to the oneemployed in conjunction with the two aforementioned methods, a seismicdata volume is divided in at least one horizontal time slice and thelatter is then divided in a great number of three-dimensional analysiscells, whereby each cell comprises two predetermined lateral directionsthat are perpendicular in relation to one another, and at least fiveseismic trace sections that are arranged therein next to each other. Asemblance value of the trace sections present in the cell is assigned tothe corresponding data point in the respective cell. In saidconjunction, the semblance is a known measure for the correspondenceamong seismic trace sections. By searching various earth bedinclinations and directions, the incidence and the direction ofincidence of the analyzed reflector are then determined based on thebest coherence. The computed inclination data are subsequently displayedfor each cell in addition to the semblance value as well.

The three evaluation methods specified above do in fact permitsupporting the data interpretation in an automated manner; however, thehigher objectivity in the interpretation achieved in that way is tradedfor substantial expenditure required for computing the seismic data.

An image processing method is known from the presentation of the DGMKDeutsche Wissenschaftliche Gesellschaft für Erdöl, Erdgas und Kohle e.V.[German Scientific Society for Oil, Natural Gas and Coal],Tagungsbericht [Proceedings] 9601 (1996) by C. HELLMICH, H. TRAPPE andJ. FERTIG, which is titled “Bildverarbeitung seismischer Attribute undGeostatistik im Oberkarbon” [Image Processing of Seismic Attributes andGeostatistics in the Upper Carboniferous]. Said method permits aquantitative characterization of seismic representations and thusfurther interpretations of the lithology. Different image processingfilters are employed in said process on amplitude charts, and thevariations or the continuity of the local neighborhood are quantified.Said filters represent 2-D multi-trace filters, and the localenvironment surrounding a data point is evaluated with the help of suchfilters. Operators employed for said purpose include the entropy and thedispersion operators, among others. Charts can be produced for theinterpretation with all attributes. The “entropy” or “dispersion”quantities are in this conjunction dimensional figures that quantify thevariations or continuities of the amplitude within the localenvironment.

Application of the aforementioned methods for large areas is frequentlyexcluded for cost reasons.

U.S. Pat. No. 5,432,751 and U.S. Pat. No. 5,153,858 describe theassignment of the value “0” or “1” to a sample, whereby such allocationonly serves the purpose of marking and quickly finding again points inthe seismic signal showing a defined characteristic. In said process,the purpose of such markings is to combine said points at a later timein a semi-automated process in a geological horizon (automatic picking),whereby the reduction in memory locations achieved through suchassignment permits interactive processing of the entirety of thecharacteristic points. Therefore, the set of measured seismic data isfirst compared based on a defined characteristic and then markedaccording to the result of the corresponding horizon by “1”. Said dataset then exclusively serves for quickly finding again corresponding datapositions of the original seismic data, which are substantially morecomprehensive.

SUMMARY OF THE INVENTION

The problem of the invention is to propose a processing method for datasets of seismic measurements by which it is made possible to identifygeological structures such as, for example faults or bed displacements,and also the stratigraphic, lithological and petrological conditions,with the lowest possible expenditure in terms of computing, paired atthe same time with high objectivity of the results.

Said problem is solved with the method according to claims 1, 2 or 3.

It is essential to the invention in this conjunction that the set ofmeasured data to be investigated, which data set comprises a greatnumber of rows of data points occupied by amplitude values, such rowsbeing time rows, as a rule, is converted into a binary data set, wherebya binary value “0” or “1” is assigned to each data point instead of thediscrete amplitude value comprising several bytes. The discreteamplitude value is compared in this conjunction to a predeterminedthreshold value and assigned the number “0” if the amplitude value islower versus the threshold value, or otherwise assigned the number “1”.The amplitude information thus is binarized. The amount of data isreduced by the factor 32, for example in connection with the usualamplitude resolution of 4 bytes.

The binary data set so generated is subsequently subjected to asimilarity analysis in an environment defined by a predetermined cellsize, where the semblance of the binary data present in the cell isanalyzed for each data point and the associated central data point isassigned a quantity reflecting the semblance.

The computing time is reduced vis-a-vis comparable methods ofinterpretation by about 97% because the computation has to be carriedout only with binary quantities. Furthermore, the data of the resultrequire reduced memory locations versus comparable methods because 1byte suffices for representing the attribute “semblance”, as a rule.Furthermore, due to the standardization of the binarization, the methodis not depending on the level, so that no scaling problems occur withthe representation of the result. Moreover, the result is moreindependent with respect to possible processing errors.

The data set generated as defined by the invention can be represented inthe usual horizontal or vertical sections (slices and profiles), forexample in gray levels or with color coding. Such charts and profilesshow a clear reproduction of the geological structures such as, forexample the localization of salt overhangs, the position and orientationof faults, bed and block displacements, horst and trench structuresetc., and thus supply an instrument for assessing the underground. Inparticular, it is possible to determine on the basis of the data setsprocessed as defined by the invention hydrocarbon deposits, for examplesites where oil and natural gas are trapped, and in general the lateralas well as also the vertical distribution of oil and natural gasdeposits.

The value reflecting the similarity is computed by said method bycounting the data points with the same binary value (“0” or “1” in eachcase) within the entire environmental cell, whereby the highest numberis assigned to the central data point in the newly generated data set.High values reflect in this connection correspondence of the data valuesin the cell being viewed.

As an alternative, the determination of the similarity value is carriedout by counting in each case the data points having the same binaryvalue, “0” or “1”; however, separately for each horizontal slice of thecell having a data point. In an intermediate step, the greater number isassigned to the slice as the similarity value. According to saidintermediate step, the sum of the individual values is assigned to thecentral data point in the newly generated data set. Horizontal weightingis taken into account with such a similarity analysis.

As a further alternative, weighting in the linear, vertical directioncan be achieved along the respective seismic trace in that the datapoints having the same binary value, “0” or “1” in each case, arecounted, but counted separately for each trace of the cell, i.e. foreach binary time series of the latter. In an intermediate step, thegreater number is then assigned to the trace as the similarity value.The sum of the individual, trace-related values of a cell is thenassigned to the central data point in the newly generated data set.

As the usual amplitude values of seismic traces vary between +X and −X,whereby X is a maximally representable amplitude value, a thresholdvalue around 0 would statistically supply an about equally weigheddivision of “0” and “1” in the binary data set generated in thebinarization process. Preferably, however, a value is pre-adjusted thatis by a few bits (LSB) greater or smaller than the threshold value.

Alternatively, it is possible to determine as the threshold value theamplitude value resulting prior to the binarization process as the mostfrequently occurring value from a histogram analysis of the set ofmeasured data, or from a cutout therefrom.

If the cell approximated to the data point being processed comprises arectangular/squared stone-shaped or elliptic/ellipsoidal environment,the result of the analysis will be obtained as much balanced aspossible. Cube-shaped of spherically shaped cells are preferred in thisconnection.

A cell size that is suitable for many applications consists of 5×5 datapoints in connection with a 2-D data set, or of 5×5×5 data points with a3-D data set.

A statically and dynamically corrected, stacked and migrated seismicmeasurement data set is preferably used as the starting point. Furtherprocessed sets of measured data, for example additionally filtered anddepth-converted data sets can be employed as well. Likewise, theapplication on unstacked data, for example single-shot combinations(shotgather) and CMP-gather is possible. This includes derived seismicattributes such as acoustic impedance (from the seismic inversion) andAVO-attributes (e.g. AVO-gradient, AVO-intercept) as well.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is described in greater detail in the following with thehelp of an exemplified embodiment and by reference to the attacheddrawings, in which

FIG. 1 is a schematically represented 3-D data volume with a cell markedby way of example.

FIG. 2 shows the top edge of the earth bed from a data set processed asdefined by the invention in the form of a chart representation;

FIG. 3 shows the top edge of another earth bed of the same measured areaby a representation as in FIG. 2; and

FIG. 4 is a flow chart showing the process according to the invention.

DETAILED DESCRIPTION OF THE DRAWINGS

A 3-D data set is shown in FIG. 1 in the form of a squared block-shapedvolume 1. A great number of seismic traces, which are not shownindividually, are combined in the 3-D data volume. The seismic traceshave been preferably obtained in this connection from measured seismicprofiles that are covered multiple times, by static and dynamiccorrections with subsequent stacking and migration. Each seismic tracethus corresponds in this connection with a time series extending in thedirection of the Z-axis (depth), whereby a digital amplitude value isassigned to each data point (sample). The spacing of the data points isdependent upon the scanning rate, which, in the field of seismology,usually amounts to 1, 2 or 4 milliseconds. The spacing of the traces inthe lateral direction (X- and Y-directions) is dependent on the geometryof the layout and the shot frequency and usually amounts to 12.5 m, 25 mor 50 m.

Now, as shown in the process in FIG. 4, each amplitude value of a datapoint in the data volume is first compared with a threshold value,denoted by “0” or by “1” if it exceeds the threshold value. A binarydata set is thus generated with the same number and arrangement of datapoints, with only one binary values (either “0” or “1”) being assignedto each data point.

Now, a neighborhood cell is preselected for further processing, suchfurther processing comprising for each central data point viewed adefined number and geometric arrangement of neighboring data points.

The size of the cell may be selected in this connection in any desiredmagnitude both in the lateral direction (X- and Y-directions) and thevertical direction. The shape of the cell is not fixed either. Becauseof the three-dimensional matrix of the data points, a squaredstone-shaped or cube-shape form of the cell appears preferably. However,approximately cylindrical, ellipsoidal or parallelepiped-shaped cellsmay be preselected as well. The selection of the cell size is dependentupon the geological conditions reflected in the data set, on the onehand, and on the geometric/time matrix spacing of the data points in thedata set on the other.

A cell 2 in the form of a cube comprising 3×3×3 data points is shown inFIG. 1 for illustration purposes. Such a cell 2 comprises 27 binary datapoints 20 in the cell 2. The data points located directly adjacentaround a central data point 21 are thus taken into account in theevaluation of said central data point 21 as well.

It is emphasized at this point that a cell size of 5×5×5 was found to beparticularly advantageous in practical applications. The cell sizerepresented in FIG. 1 especially serves for illustration purposes.

Now, said pre-adjusted environmental cell is formed for each data pointcontained in the considered data volume for the subsequent similarityanalysis. An evaluation is carried out along the edges of the data setonly for data points for which the environmental cell is completelycontained in the data volume. Alternatively, it is possible also to addzero traces along the edges so as to be able to compute similarityvalues for data points disposed on the edge of the original data set aswell, whereby the similarity value is influenced by the added-on zerotraces.

In the similarity analysis, a value reflecting the similarity in therespective environmental cell is then assigned to each central orcenter-point data point. A data set with the same data point matrix isthus generated from the binary data set, whereby a similarity value isassigned to each data point as the attribute.

FIG. 2 shows a chart representation of the top edge of a layer followingprocessing by the method as defined by the invention.

In said representation, the top edge of said layer was marked (picked)in the 3-D data set volume in the usual seismic data evaluation.

For seismic data processing as defined by the invention, the 3-D datavolume was converted as described above into a binary data set. Now, thesimilarity analysis for the data points representing the top edge of thelayer was carried out along the marked top edge of the layer having adata point. In the exemplified embodiment, use was made of a cell sizeof 5×5×7 data points in the X-, Y- and Z-directions. Said environmentwas viewed for each data point along the top edge of the layer. In saidprocess, the greater number of equal binary values in the viewed cellwas assigned to the considered central data point as the similarityvalue. The range of the values thus comprises natural numbers from 88 to175, which are accordingly shown in the chart representation in FIG. 2on the right-hand side by gray shades.

FIG. 3 is a corresponding chart representation of another top edge of alayer, which is generated by processing in accordance with FIG. 2.

In both figures, drilling locations present in the measured area aremarked by capital letters.

The data sets shown in the form of charts and processed as defined bythe invention facilitate the interpretation of subterranean data. Forexample, in FIGS. 2 and 3, fault zones can be clearly identified in thesites marked by the arrows 3. Such fault zones can be located inconventionally processed 3-D data sets only with difficulty.

The application of the method as defined by the invention permitsachieving enhanced identification of geological structures as well as ofthe stratigraphic, lithological and petrological conditions. Thecomputing expenditure is minimized in this connection by binarizing theamplitude values, as well as with the help of the similarity analysis,which is realized with simple computing rules.

What is claimed is:
 1. A method for efficiently processing a seismic 2-D or 3-D measurement data set comprising a large number of seismic traces, wherein each data set comprises a series of data points having amplitude values, the method comprising the steps of: a) determining a threshold value for the series of data points; b) converting the set of measured data into a set of binary data comprising a plurality of binarized data points in which each data point is assigned either a number “0” if an amplitude value is lower than said threshold value or a number “1” if an amplitude value is equal or greater than said threshold value; c) forming for each binarized data point a cell of a predetermined cell size from said plurality of binarized data points wherein each cell defines an environment including a central data point; and d) conducting a similarity analysis on each cell wherein each of said cells has said central data point and said central data point is assigned a similarity value reflecting the degree of similarity of binary data values in its respective cell wherein said similarity value corresponds to the quantity of the greatest number of the data points in the cell with the same binary value.
 2. The method according to claim 1, wherein said threshold value has an initial value that is slightly greater or smaller than zero.
 3. The method according to claim 1, wherein said step of determining a threshold value includes subjecting the set of measured data or a cutout therefrom to a histogram analysis prior to the binarization, and wherein the most frequently occurring amplitude value is used as the threshold value.
 4. The method according to claim 1, wherein the cell associated with each processed central data point comprises an approximately rectangular/squared stone-shaped or an elliptic/ellipsoidal environment.
 5. The method according to claim 1, wherein the cell size is preadjusted to 5×5 (×5) data points.
 6. The method according to claim 1, wherein the seismic measurement data set is statically and dynamically corrected.
 7. The method according to claim 1, wherein the Seismic measurement data set is time- or depth-migrated.
 8. The process as in claim 1, wherein said step of converting said measured data into binary data is performed to reduce the size of the measured data in the data set, and wherein said step of converting a set of measured data into a set of binary data and said step of conducting a similarity analysis, reduces the computing time by approximately 97%.
 9. A method for efficiently processing a seismic 2-D or 3-D measurement data set comprising a large number of seismic traces, wherein each data set comprises a series of data points having amplitude values, the method comprising the steps of: a) determining a threshold value for the series of data points; b) converting the set of measured data into a set of binary data comprising a plurality of binarized data points in which each data point is assigned either a number “0” if an amplitude value is lower than said threshold value or a number “1” if an amplitude value is equal or greater than said threshold value; c) forming for each binarized data point a cell of a predetermined cell size from said plurality of binarized data points wherein each cell defines an environment including a central data point; and d) conducting a similarity analysis on each cell, wherein said central data point for each of said plurality of cells is assigned a similarity value reflecting the degree of similarity of binary data values in each of said plurality of separate cells and wherein said similarity value corresponds to a sum of each quantity of the greater number of data points with the same binary values in each horizontal plane of each cell.
 10. The process as in claim 9, wherein said step of converting said measured data into binary data is performed to reduce the size of the measured data in the data set, and wherein said step of converting a set of measured data into a set of binary data and said step of conducting a similarity analysis, reduces the computing time by approximately 97%.
 11. A method for efficiently processing a seismic 2-D or 3-D measurement data set comprising a large number of seismic traces, wherein each data set comprises a series of data points having amplitude values, the method comprising the steps of: a) determining a threshold value for the series of data points; b) converting the set of measured data into a set of binary data comprising a plurality of binarized data points in which each point is assigned either a number “0” if an amplitude value is lower than said threshold value or a number “1” if an amplitude value is higher than said threshold value; c) forming for each binarized data point a cell of a predetermined cell size from said plurality of binarized data points wherein each cell defines an environment including a central data point; and d) conducting a similarity analysis on each cell wherein a central data point for each of said plurality of cells is assigned a similarity value reflecting the degree of similarity of binary data values in each of said plurality of cells, wherein said similarity value corresponds to a sum of each quantity of the greater number of data points with the same binary value in each trace.
 12. The process as in claim 11, wherein said step of converting said measured data into binary data is performed to reduce the size of the measured data in the data set, and wherein said step of converting a set of measured data into a set of binary data and said step of conducting a similarity analysis, reduces the computing time by approximately 97%. 